Lifeboat Foundation

Now, researchers are homing in on an artificial photosynthesis device that could let us do the same trick, turning sunlight and water into clean-burning hydrogen fuel for our cars, homes, and more.

Solar cells already let us convert sunlight into electricity. Artificial photosynthesis devices, however, use sunlight to turn water or carbon dioxide into liquid fuels, such as hydrogen or ethanol.

These can be stored more easily than electricity and used in different ways, allowing them to substitute for fossil fuels like oil and gas.

The Q engineer (previously) who likes to fiddle around with bicycle tires, decided to remove the entire internal hub (spokes and frame) from the wheel of his bicycle. He then crafted, molded, and welded an entire external system to ensure the ride would be smooth.

I doubt they were the first to use artificial intelligence in war. But it does discuss the AI technologies used in the recent conflict.

They used AI technology to identify targets for air strikes, specifically to counter the extensive tunnel network of their opponents.

Continue reading “Israel Defense Forces (IDF) have claimed the world’s first use of AI & supercomputing in war!” »

An outstanding idea, because for one there has been a video/ TV show/ movie, etc… showing every conceivable action a human can do; and secondly the AI could watch all of these at super high speeds.

Predicting what someone is about to do next based on their body language comes naturally to humans but not so for computers. When we meet another person, they might greet us with a hello, handshake, or even a fist bump. We may not know which gesture will be used, but we can read the situation and respond appropriately.

In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.

Continue reading “AI learns to predict human behavior from videos” »

Electric motors have a lot of power and it is instant on-demand.

When Tesla unveiled its new Plaid Model S earlier this month, it took the world by storm, fueling dreams of speed, racing, and fun. In the sense of a grand finale to Tesla’s event, another event was talked about in the weeks following. That event was the annual race to the summit of Pikes Peak. Also known as “The Race to the Clouds,” the entire track winds through 156 turns over 12.42 miles as it reaches the summit of Pikes Peak.

Continue reading “Tesla Model S Plaid Defeats Cars Built To Win Pikes Peak — ‘Nobody would expect us to be faster than that.’” »

There’s no motorcycle on the planet like this one. British company White Motorcycle Concepts (WMC) has put land speed record holders on notice with a 2WD, hydraulically hub-steered electric motorcycle, designed around a giant hole. The company says the WMC250EV should be capable of more than 250 mph (402 km/h) thanks to a massive 69 percent reduction in drag.

Rob White has paid his dues in the racing world, working on numerous Formula One, Le Mans Prototype, V8 supercar and World Endurance Championship race teams over the last 25-odd years. And his approach to motorcycle design is clearly influenced by the world of high-end cars.

Continue reading “‘World’s fastest electric motorcycle’ uses radical big hole technology” »

Researchers at Google Brain announced a deep-learning computer vision (CV) model containing two billion parameters. The model was trained on three billion images and achieved 90.45% top-1 accuracy on ImageNet, setting a new state-of-the-art record.

The team described the model and experiments in a paper published on arXiv. The model, dubbed ViT-G/14, is based on Google’s recent work on Vision Transformers (ViT). ViT-G/14 outperformed previous state-of-the-art solutions on several benchmarks, including ImageNet, ImageNet-v2, and VTAB-1k. On the few-shot image recognition task, the accuracy improvement was more than five percentage-points. The researchers also trained several smaller versions of the model to investigate a scaling law for the architecture, noting that the performance follows a power-law function, similar to Transformer models used for natural language processing (NLP) tasks.

First described by Google researchers in 2017, the Transformer architecture has become the leading design for NLP deep-learning models, with OpenAI’s GPT-3 being one of the most famous. Last year, OpenAI published a paper describing scaling laws for these models. By training many similar models of different sizes and varying the amount of training data and computing power, OpenAI determined a power-law function for estimating a model’s accuracy. In addition, OpenAI found that not only do large models perform better, they are also more compute-efficient.

The AIR program was run by a company called Persistent Surveillance Systems with funding from two Texas billionaires. The city police department admitted to using planes to surveil Baltimore residents in 2016 but approved a six-month pilot program in 2020, which was active until October 31st.

The city of Baltimore’s spy plane program was unconstitutional, violating the Fourth Amendment protection against illegal search, and law enforcement in the city cannot use any of the data it gathered, a court ruled Thursday. The Aerial Investigation Research (or AIR) program, which used airplanes and high-resolution cameras to record what was happening in a 32-square-mile part of the city, was canceled by the city in February.

Local Black activist groups, with support from the ACLU, sued to prevent Baltimore law enforcement from using any of the data it had collected in the time the program was up and running. The city tried to argue the case was moot since the program had been canceled. That didn’t sit well with civil liberties activists. “Government agencies have a history of secretly using similar technology for other purposes — including to surveil Black Lives Matter protests in Baltimore in recent years,” the ACLU said in a statement Thursday.

Continue reading “Baltimore spy plane program was invasion of citizens’ privacy, court rules” »

Without GPS, autonomous systems get lost easily. Now a new algorithm developed at Caltech allows autonomous systems to recognize where they are simply by looking at the terrain around them—and for the first time, the technology works regardless of seasonal changes to that terrain.

Details about the process were published on June 23 in the journal Science Robotics.

The general process, known as visual terrain-relative navigation (VTRN), was first developed in the 1960s. By comparing nearby terrain to high-resolution satellite images, autonomous systems can locate themselves.